Few-Shot Learning for Rooftop Detection in Satellite Imagery

Deep Learning Tutorial

Giorgio Coppala, Nadine Daum, Elena Dreyer, Nico Reichardt

Tentative Structure (delete me)

  • problem setting (policy relevance)

  • dataset geneva

  • model & methods

  • prototypical Networks

  • main notebook in detail

  • results to expect

  • wrap-up

Problem Setting

  • Tutorial Task: Why rooftop segmentation matters (solar planning, urban policy)

  • Cities need rooftop maps for solar PV planning

  • Manual labeling is expensive

  • Few-shot learning reduces annotation cost

  • Geneva dataset comes in (example for cities)

Dataset: Geneva

  • Satellite Images: High-resolution RGB satellite images of Geneva

  • Switzerland Segmentation Labels: Binary masks indicating rooftop locations

  • Resolution: Images at various resolutions suitable for few-shot learning

  • Train/test sizes (val?)

  • Example image + mask

  • Overlay visualization (the ones you already generated!)

  • Challenges: small rooftops, shadows, label noise, class imbalance

Insert & dont forget: visuals notebook data preprocessing etc

Model & Methods

  • Data Preprocessing

  • Model Architecture

  • Few-Shot in a Nutshell (modified figure from paper)

  • Few-Shot in implementation (ntoebook reference/ pseudocode for logic?)

  • Training strategy

  • Loss function

  • Evaluation metrics

Prototypical Networks

Insert modified figure here

  • high-level schematic (support → prototype → similarity → segmentation)

  • literature reference: SRPNet

Main Notebook in Detail

how deep should we go?

lets discuss that regarding time

(presentation should be 10 minutes, followed by 5 minutes of Q&A)

Expected Results

  • Show performance for 1-shot / 5-shot / full-data comparison

  • Show predicted masks

Open to Discuss:

  • strengths

  • weaknesses

  • failure cases (shadows, tiny rooftops)

Wrap-Up: GitHub Repo

insert more from discussion + memo here

What we have so far:

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What we still need to finalize:

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Questions to discuss in class/ lynn

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